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Found 4 papers, 1 papers with code
Learning Neural Representations for Network Anomaly Detection
Our approach is to introduce new regularizers to a classical autoencoder (AE) and a variational AE, which force normal data into a very tight area centered at the origin in the nonsaturating area of the bottleneck unit activations.
One-class Collective Anomaly Detection based on Long Short-Term Memory Recurrent Neural Networks
Instead of considering each time-step separately, the observation of prediction errors from a certain number of time-steps is now proposed as a new idea for detecting collective anomalies.
Improving Fitness Functions in Genetic Programming for Classification on Unbalanced Credit Card Datasets
We examine two fitness functions from previous studies and develop two new fitness functions to evolve GP classifier with superior accuracy on the minority class and overall.
Collective Anomaly Detection based on Long Short Term Memory Recurrent Neural Network
Most of the cur- rent research on anomaly detection is based on the learning of normally and anomaly behaviors.
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